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Heatmap-guided balanced deep convolution networks for family classification in the wild

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dc.contributor.author Aspandi, Decky
dc.contributor.author Martínez, Oriol
dc.contributor.author Binefa i Valls, Xavier
dc.date.accessioned 2021-03-19T07:17:54Z
dc.date.available 2021-03-19T07:17:54Z
dc.date.issued 2019
dc.identifier.citation Aspandi D, Martinez O, Binefa X. Heatmap-guided balanced deep convolution networks for family classification in the wild. In: Proceedings of the 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019); 2019 May 14-18; Lille, France. Piscataway (NJ): IEEE; 2019. DOI: 10.1109/FG.2019.8756557
dc.identifier.uri http://hdl.handle.net/10230/46854
dc.description Comunicació presentada a: Proceedings of the 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019) celebrat del 14 al 18 de maig a Lille, France.
dc.description.abstract Automatic kinship recognition using Computer Vision, which aims to infer the blood relationship between individuals by only comparing their facial features, has started to gain attention recently. The introduction of large kinship datasets, such as Family In The Wild (FIW), has allowed large scale dataset modeling using state of the art deep learning models. Among other kinship recognition tasks, family classification task is lacking any significant progress due to its increasing difficulty in relation to the family member size. Furthermore, most current state of-the-art approaches do not perform any data pre-processing (which try to improve models accuracy) and are trained without a regularizer (which results in models susceptible to overfitting). In this paper, we present the Deep Family Classifier (DFC), a deep learning model for family classification in the wild. We build our model by combining two sub-networks: internal Image Feature Enhancer which operates by removing the image noise and provides an additional facial heatmap layer and Family Class Estimator trained with strong regularizers and a compound loss. We observe progressive improvement in accuracy during the validation phase, with a state of the art results of 16.89% is obtained for the track 2 in the RFIW2019 challenge and 17.08% of familly classification task on FIW dataset.
dc.description.sponsorship This work is partly supported by the Spanish Ministry of Economy and Competitiveness under project grant TIN2017- 90124-P, the Ramon y Cajal programme, the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and the donation bahi2018-19 to the CMTech at the UPF.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof Proceedings of the 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019); 2019 May 14-18; Lille, France. Piscataway (NJ): IEEE; 2019
dc.rights © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/FG.2019.8756557
dc.title Heatmap-guided balanced deep convolution networks for family classification in the wild
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.1109/FG.2019.8756557
dc.subject.keyword Training
dc.subject.keyword Heating systems
dc.subject.keyword Task analysis
dc.subject.keyword Data models
dc.subject.keyword Deep learning
dc.subject.keyword Pipelines
dc.subject.keyword Face recognition
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/TIN2017-90124-P
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/MDM-2015-0502
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

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